Automated health data acquisition, processing and communication system

ABSTRACT

A unique health score computation method is disclosed which masks underlying health statistics, yet provides a benchmark for a variety of applications. A system and method for collecting health related information, processing the information into a composite numerical value, and publishing the value is provided. The system includes a computer having a processor, memory, and code modules executing in the processor for implementation of the method. Information concerning a plurality of intrinsic and extrinsic parameters of a user is collected. Weighting factors are applied to the parameter in order control the relative affect each parameter has on the user&#39;s calculated numerical. The health score is computed using the processor by combining the weighted parameters in accordance with an algorithm. The numerical value is published to a designated group via a portal, while the underlying parameters remain private. In one implementation, the portal is an internet based information sharing forum.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/877,059, filed Apr. 23, 2013, which is a National Stage ofPCT/US2011/053971, filed Sep. 29, 2011 and claims the benefit of U.S.Patent Application Ser. No. 61/387,906, filed Sep. 29, 2010, and U.S.Patent Application Ser. No. 61/495,247, filed Jun. 9, 2011, each ofwhich are hereby incorporated by reference in their entireties.

FIELD OF THE INVENTION

The present invention concerns a computer implemented system for theacquisition of medical data and its processing for diagnostic,benchmarking, analytics and redistribution purposes. More particularly,the invention concerns a computer implemented system and method foracquisition, diagnosis, benchmarking, analytics and/or redistribution ofmedical data.

BACKGROUND OF THE INVENTION

Despite advances in many areas of technology, there are still barriersto assessing the relative health of a person in a rapid, cost effective,and timely manner. With the increase in health care costs and prevalenceof diseases related to unhealthy lifestyles such as diabetes and heartdisease, it is important to assess the relative health of individuals,and this has not been adequately addressed. In many areas of the world,access to doctors is limited. Even in the developed world, a doctor'stime is considered a precious commodity and there are often long waitinglists and doctor-to-specialist referral systems have to be navigatedbefore being seen. In more developed countries the ratio of doctors tothe population may be on the order of 1:1,000 persons, while in lessdeveloped countries the ratio may be 1:100,000. There are also costbarriers to having access to a doctor because an appointment with adoctor can be very expensive, especially if an individual does not haveany health insurance or lacks sufficient coverage. Accordingly, it canbe very difficult to gain access to medical professionals in order toreceive information about one's health.

Even if an individual had access to his or her health information, themechanisms for conveying that information to others is lacking ornon-existent. Privacy laws restrict the type of information that can beshared and the manner in which it can be shared. Privacy laws relatingto health information are particularly strict in regard to theinformation that can be shared. This is to protect a person fromdisclosure of sensitive information. Accordingly, the sharing of healthrelated information is generally discouraged. It is also difficult toshare health related information with friends and family. Often healthinformation is only verbally conveyed by a doctor to a patient, or thepatient will only receive paper copies of lab test results. Systems arelacking for easily sharing such information with others, especially withlarge groups of persons located in geographically remote locations.

Prior art systems that provide a limited type of numerical score whichis related to a person's health have been disclosed. For example, U.S.Patent Publication No. 2009/0105550 to Rothman et al. discloses a systemand method for providing a health score for a patient. However, thisdisclosure is primarily directed to calculating a health score of apatient in a hospital, post surgery, and the health score is based onmedical data measured from the patient (e.g., blood pressure,temperature, respiration, etc.). This method fails to take into accountthe extrinsic activities of the patient, such as the daily physicalexercise activities of the patient. U.S. Patent Publication No.2005/0228692 to Hodgdon discloses a system that calculates a healthscore based on measured medical data and can include a self assessmentsurvey, which can include surveying a participant's exercise habits.However, this only takes into account a person's purported habits, notthe actual exercise activity that a person engages in each day.Accordingly, the score is static and does not change in relation toactual activity performed.

Such disclosed systems are primarily directed to medical practitionersfor addressing issues in continuity of care and require input frompractitioners in order to produce and maintain scores. Clearly, whilethe attention of a medical practitioner is needed in emergency andcritical care situations, cost and resource factors mean that suchsystems are usable only in such situations and such systems do notaddress the general issues discussed above. Additionally, the score isonly relevant to the particular instant in time at which it was lastupdated by the medical practitioner.

SUMMARY OF THE INVENTION

According to an aspect of the present invention, there is provided acomputer implemented method for processing private health related datainto a masked numerical score suitable for publishing. The methodcomprises receiving data into a memory on a plurality of intrinsicmedical parameters and extrinsic physical activity parameters of a user.The received data and weighting factors are stored in the memory. Thereceived data is processed by executing code in a processor thatconfigures the processor to apply the weighting factors to the intrinsicmedical parameters and the extrinsic physical activity parameters. Theweighting factors for at least the extrinsic physical activityparameters include a decay component arranged to reduce the relativeweight of the extrinsic physical activity parameters for a physicalactivity in dependence on at least one factor associated with the user.The processed data concerning the intrinsic medical parameters and theextrinsic physical activity parameters are transformed by further codeexecuting in the processor into a masked composite numerical value inwhich the code is operative to combine the weighted parameters inaccordance with an algorithm. The masked composite numerical value isautomatically published to a designated group via a portal (such as asocial web site) using code executing in the processor and free of anyhuman intervention. Meanwhile, the collected information concerning theintrinsic medical parameters and the extrinsic physical activityparameters is maintained private.

According to a further aspect of such a method as can be implemented ina particular embodiment thereof, the factor associated with the user canbe an age or an age range of the user such that the decay componentreduces the relative weight of the extrinsic physical activityparameters for a first user of a first age or age range differently thana second user of a second age or age range.

According to still another aspect of such a method as can be implementedin a particular embodiment thereof, the published masked compositenumerical value can comprise an average of a group of users to arrive ata group composite numerical value determination using further codeexecuting in the processor.

According to an additional aspect of the present invention, there isprovided a computer implemented health monitoring system which comprisesa communication unit operable to receive data on a plurality ofintrinsic medical parameters and extrinsic physical activity parametersof a user. A memory is arranged to store the received data and to storeweighting factors. Also, a processor is arranged to process the receiveddata by executing code that configures the processor to apply theweighting factors to the intrinsic medical parameters and the extrinsicphysical activity parameters. The weighting factors for at least theextrinsic physical activity parameters include a decay componentarranged to reduce the relative weight of the physical activityparameters for a physical activity in dependence on at least one factorassociated with the user. The processor is further arranged to executecode to transform the processed data concerning the intrinsic medicalparameters and the extrinsic physical activity parameters into a maskedcomposite numerical value using the processor by combining the weightedparameters in accordance with an algorithm. A portal is arranged topublish the masked composite numerical value to a designated group whilemaintaining the collected information concerning the intrinsic medicalparameters and the extrinsic physical activity parameters private.

Such a system can preferably be configured so that the factor associatedwith the user can be an age or an age range of the user such that thedecay component reduces the relative weight of the extrinsic physicalactivity parameters for a first user of a first age or age rangedifferently than a second user of a second age or age range.

An embodiment in accordance with further aspects of the invention cancomprise a system that communicates either the processed data or themasked composite numerical value to an exercise machine. The machineworks in conjunction with the system through programming thereat toautomatically establish an exercise program on the basis of thecommunicated data or the masked composite numerical value. Preferably,the system so-configured receives from the exercise machine into itsmemory activity information for inclusion among the extrinsic physicalactivity parameters.

Embodiments of the present invention seek to combine data from multiplemedical and non-medical sources in a system and method that produce anormalized score for a person that takes into account available medical,physical activity and optionally lifestyle data (such as diet) in anarrangement that can be operated and updated in substantially real-timeand does not need frequent access to a medical practitioner. The scoreand trends associated with it can be used for various purposes includingtriggering alerts as to possible medical issues or repercussions,providing user feedback, automated motivation and/or goal setting,training scheduling, automated referrals for medical analysis. Among thealerts that can be generated are alerts that are triggered based onmonitoring of a composite numerical value of a health score that iscomputed, the computed value of which can cause a feedback communicationto be sent to the user (e.g., within the system portal or by email, SMS,etc.), as a result of code executing in a processor and without humanintervention, if the monitoring detects a change in the user's scoresuch as due to decay in value by operation of the algorithm, orreduction in value due to eating habits, or in fulfillment of goalsinput into the system by the user or by a group the user has associatedwith, or as part of a non-user-specific goal program that the system canhave to motivate wellness (e.g., good exercise or eating habits).Embodiments of the present invention apply a weighting factor to therespective physical activity and/or lifestyle data such that recentevents have a greater impact on the score than those that occurredfurther in the past.

In the described embodiments, a unique health score computation methodis disclosed which masks underlying health statistics, yet provides abenchmark for a variety of applications. In one embodiment, a method forcollecting and presenting health related data is provided. The methodincludes collecting information concerning a plurality of intrinsicmedical parameters and extrinsic physical activity parameters of a user.The collected information is stored in a memory and weighting factorsare stored in the memory. The collected information is processed byexecuting code in a processor that configures the processor to apply theweighting factors to the intrinsic medical parameters and extrinsicphysical activity parameters. The collected information concerning theintrinsic medical parameters and extrinsic physical activity parametersis transformed into a masked composite numerical value using theprocessor by combining the weighted parameters in accordance with apredetermined algorithm. The masked composite numerical value ispublished to a designated group via a portal while maintaining thecollected information concerning the intrinsic medical parameters andextrinsic physical activity parameters private.

Preferred embodiments of the present invention seek to provide anormalized rating system that can provide an assessment of the relativehealth of an individual that can be used as the basis of a faircomparison to other individuals having different ages, sex, medicalstatus or lifestyles.

Various features, aspects and advantages of the invention can beappreciated from the following Description of Certain Embodiments of theInvention and the accompanying Drawing Figures.

DESCRIPTION OF THE DRAWING FIGURES

FIG. 1 is a schematic block diagram of a local health informationcollection and communication system according to a first implementationof the invention;

FIG. 1A is a network diagram according to another implementation of theinvention;

FIG. 2 is a schematic flow diagram according to one embodiment of theinvention;

FIGS. 3 a-3 e are screen shots of a user interface according to oneembodiment of the invention;

FIG. 3 f is an illustration of progressions over time of parameters usedto determine the health score in one embodiment of the invention;

FIG. 4 a is an illustration of a data presentation format according toone embodiment of the invention;

FIG. 4 b is an illustration of a data presentation format according toone embodiment of the invention;

FIG. 4 c is an illustration of a data presentation format according toone embodiment of the invention; and

FIG. 4 d is an illustration of a data presentation format according toone embodiment of the invention.

DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS OF THE INVENTION

By way of overview and introduction, the present invention is describedin detail in connection with a distributed system in which dataacquisition, data storage, and data processing are used to produce anumerical score as a basis for assessing the relative health of a user.

In one implementation, a system 100 includes a computer-basedapplication for the collection of health related parameters of a userand a user interface 110 for the display of data. The computer-basedapplication is implemented via a microcontroller 120 that includes aprocessor 124, a memory 122 and code executing therein so as toconfigure the processor to perform the functionality described herein.The memory is for storing data and instructions suitable for controllingthe operation of the processor. An implementation of memory can include,by way of example and not limitation, a random access memory (RAM), ahard drive, or a read only memory (ROM). One of the components stored inthe memory is a program. The program includes instructions that causethe processor to execute steps that implement the methods describedherein. The program can be implemented as a single module or as aplurality of modules that operate in cooperation with one another. Theprogram is contemplated as representing a software component that can beused in connection with an embodiment of the invention.

A communication subsystem 125 is provided for communicating informationfrom the microprocessor 120 to the user interface 110, such as anexternal device (e.g., handheld unit or a computer that is connectedover a network to the communication subsystem 125). Information can becommunicated by the communication subsystem 125 in a variety of waysincluding Bluetooth, WiFi, WiMax, RF transmission, and so on. A numberof different network topologies can be utilized in a conventionalmanner, such as wired, optical, 3G, 4G networks, and so on.

The communication subsystem can be part of a communicative electronicdevice including, by way of example, a smart phone or cellulartelephone, a personal digital assistant (PDA), netbook, laptop computer,and so on. For instance, the communication subsystem 125 can be directlyconnected through a device such as a smartphone such as an iPhone,Google Android Phone, BlackBerry, Microsoft Windows Mobile enabledphone, and so on, or a device such as a heart rate or blood pressuremonitor (such as those manufactured by Withings SAS), weight measurementscales (such as those manufactured by Withings SAS), exercise equipmentor the like. In each instance, the devices each comprise or interfacewith a module or unit for communication with the subsystem 125 to allowinformation and control signals to flow between the subsystem 125 andthe external user interface device 110. In short, the communicationsub-system can cooperate with a conventional communicative device, orcan be part of a device that is dedicated to the purpose ofcommunicating information processed by the microcontroller 120.

When a communicative electronic device such as the types noted above areused as an external user interface device 110, the display, processor,and memory of such devices can be used to process the health relatedinformation in order to provide a numerical assessment. Otherwise, thesystem 100 can include a display 140 and a memory 150 that areassociated with the external device and used to support datacommunication in real-time or otherwise. More generally, the system 100includes a user interface which can be implemented, in part, by softwaremodules executing in the processor of the microcontroller 120 or undercontrol of the external device 130. In part, the user interface can alsoinclude an output device such as a display (e.g., the display 140).

Biosensors 115 can be used to directly collect health information abouta user and report that information. The biosensor can be placed incontact with the user's body to measure vital signs or other healthrelated information from the user. For example, the biosensor can be apulse meter that is worn by the user in contact with the user's body sothat the pulse of the user can be sensed, a heart rate monitor, anelectrocardiogram device, a pedometer, a blood glucose monitor or one ofmany other devices or systems. The biosensor can include a communicationmodule (e.g., communication subsystem 125) so that the biosensor cancommunicate, either wired or wirelessly, the sensed data. The biosensorcan communicate the sensed data to the user interface device, which inturn communicates that information to the microcontroller. Optionally,the biosensor can directly communicate the sensed the data to themicroprocessor. The use of biosensors provides a degree of reliabilityin the data reported because it eliminates user error associated withmanually, self-reported data.

Alternatively or in addition, the user can self-report his or her healthrelated information by manually inputting the data. Thus, in anotherimplementation, as shown in FIG. 1A, health related data of a person isentered directly into a computer 160 and provided across a network 170to a server computer 180. (All computers described herein have at leastone processor and a memory.)

Regardless of the implementation, the system provides a means forassigning a numerical value that represents the relative health of anindividual. The numerical value is described herein as a “health score”and can be used to assess to the individual's health based on healthrelated information collected from a user. The health score iscalculated based on the collected health information using an algorithm.The user or the communication subsystem 125 provides the system thehealth related information concerning a number of health parameters.Predetermined weighting factors are used to assign a relative value ofeach of the parameters that are used to calculate the health score. Theuser's health score is then calculated by combining the weightedparameters in accordance with an algorithm. For example, the parameterscan be a person's blood glucose level and body weight. A weightingfactor “a” is applied to the blood glucose data and a weight factor “b”can be applied to the body weight data. If the blood glucose data is amore important factor in determining a person's health than body weight,then the weighting factor “a” will be larger than weighting factor “b”so that the blood glucose data has a larger impact on the calculatedhealth score (e.g., Healthscore=Glucose*a+(Weight/100)*b). In certainimplementations, the weighting factor is a non-unity value (e.g.,greater or less than one, but not one). Fewer or additional factors canbe included in the calculation of the health score, and an offset valuecan be included that is added or subtracted or which modifies the entirecalculation, in certain implementations such as to account for age orgender as two possible reasons; however, the foregoing is intended as anon-limiting example of how to calculate a health score. Otherparameters that can be measured and included in the calculation includeblood pressure measurements, height, body mass index, fat mass, medicalconditions such as diabetes, ventricular hypertrophy, hypertension,irregular heartbeat and fasting glucose values. Where absent, aparameter can be omitted from the calculation or it can be estimatedfrom other parameters and/or values obtained from a sample group ofindividuals having similar parameters.

In addition to intrinsic medical parameters, physical activity of a useris also taken into account when calculating his or her health score.Physical activity can be monitored via an appropriate sensor dependenton the activity. Sensors can include a GPS unit, an altimeter, a depthmeter, a pedometer, a cadence sensor, a velocity sensor, a heart ratemonitor or the like. In the case of gym-based activities, computerizedexercise equipment can be configured to provide data directly on theprogram completed by the user (for example, a so-calledelliptical/cross-trainer can provide far better data on the workout thana user's pedometer etc). Although automated capture of parametersconcerning a user's physical activity is preferred, a user interface formanual activity entry is also provided. In this regard, an exercisemachine such as a treadmill, elliptical, stationary bike or weightlifting machine with a rack of weights or bands can be provided with acommunications interface to communicate with the system described hereinto provide extrinsic physical activity parameters to the system and toreceive and further include a processor configured to process data fromthe system so as to automatically adjust an exercise program at theexercise machine to meet a goal, challenge, or other objective for thatuser.

Lifestyle data such as diet, smoking, alcohol consumed and the like canalso be collected and used in calculating the health score. In oneembodiment, a barcode or RFID scanner can be used by a user to capturedata on consumed foodstuffs that is then translated at a remote system,such as the server 180 or a website in communication with the server180, into parameters such as daily calorie, fat and salt intake. Inpart, the system relies on such data being provided by the user whileother data can be obtained through data network connections oncepermissions and connectivity rights are in place.

Physical activity and lifestyle data is tracked over time and a decayalgorithm is applied when calculating its effect on the health score, asis discussed in more detail below. As such, physical activity far in thepast has a reduced positive effect on the health score. Preferably, theweighting factors used in the algorithm for the computation of thehealth score are adjusted over time in accordance with a decay componentwhich is arranged to reduce the relative weight of the parameters thatare used in the calculation. The decay component can itself comprise aweighting value, but can also comprise an equation that takes intoaccount at least one factor associated specifically with the user, suchas the user's weight or weight range, age or age range, any medicalconditions known to the system, and any of the other parameters that maybe known to the system, or a curve that is configured in view of thesefactors so that a value can be read from the curve as a function of thevalues along the axes for that user. In this way, the decay componentcan reduce the relative weight of the parameters used in the healthscore calculation for a first user differently than for another user,such as when the first user has a first age or age range and the seconduser has a second age or age range.

A central system, preferably a database and website that can be hosted,for example, by the server 180, maintains data on each user and his orher health score and associated parameters and their trends over time.The data can be maintained in such a way that sensitive data is storedindependent of human identities, as understood in the art.

The calculated health score for each user is then processed independence on a system, group or user profile at the central system.Depending on the profile settings, the health score and trendsassociated can cause various automated actions. For example, it cancause: triggering of an automated alert; providing user feedback such asa daily email update; triggering the communication of automatedmotivation, warnings and/or goal setting selected to alleviate aperceived issue; adjustment of a training programme; or automatedreferral for medical analysis.

The user's health score is also provided to a designated group ofrecipients via a communication portal. The group of recipients cancomprise selected, other, users of the system (e.g., friends and family)so that the health scores of the selected, other users can be comparedagainst the health score of still others. In alternative arrangements,all users can see other user's scores, or the group of recipients can bedefined as a specific health insurance provider so that price quotes canbe provided to insure the individual. Other possibilities are within thescope the invention.

Referring now to FIG. 2, a schematic flow diagram according to oneembodiment of the invention is described in support of an assessment ofa person (e.g., a patient or user) to provide a health score. At step210, the user initiates the process for the collection, processing, andpublishing of health related data. For example, a person using a mobileelectronic device (e.g. a smart phone or portable computing device)selects the software application, which starts the program running onthe device processor, or the user can access an Internet based web pagein which code is executed on a remote processor and served to the user'slocal device. An identification module prompts the user to identifyhimself and authenticate his identity. This can be accomplished byprompting the user to enter a user name and password, or by other means,such as a fingerprint reader, keyfob, encryption or other mechanism toensure that identity of the user. Alternatively, if the user isaccessing the system via a personal electronic device, identificationdata can be stored in the local device memory and automatically accessedin order to automatically confirm the identity of the user.

At step 220, a data collection module executing on the processor canprompt the user to provide health related data corresponding to a numberof parameters. In one implementation, one or more the parameters areprovided automatically by the communication subsystem 125. Theparameters can include the user's body weight, height, age and fitnessactivity information. Such measurable medical parameters are intrinsicparameters of the user. The user's body weight and height provideinformation about the user's current state of health. The fitnessactivity information corresponds to the amount of exercise the userengages in. This information is an example of a physically activityparameter that is an extrinsic parameter of the user. For example, theuser can enter information about his or her daily fitness activities,such as the amount of time the user engaged in physical activity and thetype of physical activity. If the user went to the gym and exercised ona bicycle for thirty minutes, for example, that information is enteredinto the system. The user's fitness activity information providesinformation about the actions that are being taken by the user in orderto improve his or her fitness.

A user's body weight, height, age and fitness activity information arejust some of the parameters for which information can be collected. Thesystem can collect and process a multitude of other parameters that canbe indicative of a user's health. For example, parameters can includeblood glucose levels, blood pressure, blood chemistry data (e.g.,hormone levels, essential vitamin and mineral levels, etc.), cholesterollevels, immunization data, pulse, blood oxygen content, informationconcerning food consumed (e.g., calorie, fat, fiber, sodium content),body temperature, which are just some of a few possible, non-limitingexamples of parameters that can be collected. Various other parametersthat are indicative of a person's health that can be reliably measuredcould be used to calculate a person's health score.

The collected health parameter information is stored in a memory at step230. At step 240, a weighting module recalls weighting factors from thememory. The weighting factors can be multiplication coefficients thatare used to increase or decrease the relative value of each healthparameters. A weighting factor is assigned to each health parameter asshown in the formulas herein. The weighting factors are used to controlthe relative values of the health parameters. Some health parameters aremore important than others in the calculation of the users health score.Accordingly, weighting factors are applied to the health parametersincrease or decrease the relative affect each factor has in thecalculation of the user's health score. For example, a user's currentbody weight can be more important than the amount of fitness activitythe user engages in. In this example, the body weight parameter would beweighted more heavily by assigning a larger weighting factor to thisparameter. At step 205, the weighting module applies the recalledweighting factors to the collected health parameter values to provideweighted health parameter values. The weighting factor can be zero inwhich case a particular parameter has no impact on the health score. Theweighting factor can be a negative value for use in some algorithms.

After the parameters have been weighted, the user's health score iscomputed at step 260 via a scoring module operating in the processor.The scoring module combines the weighted parameters according to analgorithm. In one implementation, the health score is the average of theuser's body mass index (BMI) health score and the user's fitness healthscore minus two times the number of years a person is younger than 95.The algorithm formula for this example is reproduced below:

Health Score=((BMI Healthscore+Fitness Healthscore)/2)−2*(95−Age).

The user's BMI Healthscore is a value between 0 and 1000. The BMIHealthscore is based on the user's BMI, which is calculated based on theuser's weight and height, and how much the user's BMI deviates from whatis considered a healthy BMI. A chart or formula can be used to normalizethe user's BMI information so that dissimilar information can becombined. A target BMI value is selected which is assigned a maximumpoint value (e.g. 1000). The more the user's BMI deviates from thetarget value the fewer points are awarded. The user's FitnessHealthscore is based on the physical activity or exercise of a person.In one embodiment, it is the sum of the number of fitness hours (i.e.,the amount of time the user engaged in fitness activities) in the past365 days where each hour is linearly aged over that time so that lessrecent activity is valued less. The resulting sum is multiplied by twoand is capped at 1000. This normalized the fitness information so thatit can be combined to arrive at the health score. A target daily averageof fitness activity is selected and is awarded the maximum amount ofpoints (e.g. 1000). The user is awarded fewer points based on how muchless exercise that engage in compared to the target.

In another implementation, the health score is determined from a numberof sub-scores that are maintained in parallel beyond the BMI healthscore and the fitness health score. Likewise, the health score can bedetermined using similar information in a combinative algorithm asdiscussed above using different or no age adjustments.

Intrinsic medical parameters are processed to determine a base healthscore. Extrinsic parameters such as those from physical exercise areprocessed to determine a value that is allocated to a health pool and abonus pool. The value, preferably expressed in MET hours, associatedwith a physical activity is added to both the health pool and the bonuspool. A daily decay factor is applied to the bonus pool. Any excessdecay that cannot be accommodated by the bonus pool is then deductedfrom the health pool. The amount of decay is determined dependent on thesize of the health pool and bonus pool such that a greater effort isrequired to maintain a high health and bonus pool. The health pool valueis processed in combination with the score from the intrinsic medicalparameters in order to calculate the overall health score value. Thiscan be on a similar basis to the earlier described implementation or itcan include different parameters and weighting factors. In oneembodiment, the health pool value is a logarithm or other statisticalfunction is applied to age the respective values over time such thatonly the most recent activity is counted as being fully effective to thehealth/bonus pool. An example user interface showing the health score,the health reservoir and selected other measured parameters (as it willbe appreciated that many simply combine to make up the scores) is shownin FIGS. 3 a and 3 b. Various sub-scores and their trends are recorded,as is shown in FIG. 3 c.

As will be appreciated, MET hours are kcal expended divided by kilogramsof body weight, i.e. 100 kcal expended by a person of 50 kg is 2 MET h.This is “normalized energy”, making the system fair for persons of allweights. With this method, pools can be the same size for each perperson as energy is normalized for the person based on his or her bodyweight.

In one implementation, each person is assigned a health pool having acapacity of 300 MET h and a bonus pool having a capacity of 60 MET h.

When someone performs activity A, the pools are updated as follows:

H=min(H+A*alpha,300)

B=min(B+A*(1−alpha),60)

Where H is the health pool score, B is the bonus pool score, A is theMET h value for the activity and alpha is a system wide constant(selected between 0 and 1) that determines the proportion in which theactivity contributes to the respective pools.

The activity is split between the health pool and the bonus pool. Anyexcess MET h activity going over the cap of any pool is discarded. Adaily decay value D is applied to the pools as follows:

D = f(H, B) B = B − D If B < 0: D = D + B B = 0 If D < 0: D = 0

The decay is fully applied to the bonus pool, and if the bonus pool isempty, the remainder is applied to the health pool. In this embodiment,no pool ever goes below zero.

The system finds its equilibrium where A equals f(H, B), i.e. where theaverage daily activity matches the average daily decay. The functionf(H, B) is highly non-linear with regard to H and B. In essence, ittakes sub-linearly less effort to maintain a small pool, andsuper-linearly more effort to maintain a large pool. This is to makesure that the average person can maintain a, say, half-full health pool(150, corresponding to a score of 500), whereas it takes a massivelyhigher effort (typically only delivered by a professional enduranceathlete) to maintain a full health pool (300, corresponding to a scoreof 1000). FIG. 3 f shows a simulation of the buffer pool and healthreservoir score over time assuming activity varying between 11.5 and 16MET h per day and 2 days off per week. A perfect health reservoir scoreof 1000 would require 30 MET h activity per day, as can be seen from thecurve in the top right corner of FIG. 3 f

Preferably, the health score is based on a weighted combination ofhealth factor(s) and the exercise record of the person over time. Thehealth factors can be updated regularly by the user. For example, theuser can provide health related information after every event that istracked and processed by the system. The user can update after a meal,after exercising, after weighing himself, etc. In the case of recordalof an activity/event by a sensor, portable device or the like, thecaptured/calculated parameters can be automatically uploaded and used toproduce a revised health score. For example, feedback could be providedshowing the effect of exercise while a user is running, working out onexercise equipment etc. In selected embodiments, feedback can beprovided to an administrator such as a gym staff member where it isdetermined that a user is exceeding a predetermined threshold (which dueto knowledge of their health can be varied respective to their healthscore or other recorded data). Accordingly, the health related data canbe updated in a near real-time manner.

The user can also update the information twice daily, once daily, or atother periodic times. Moreover, the health score can be based on anaverage of the information over time. Fitness activity, for example, canbe averaged over a period of time (e.g. over a week, month, or year).Averaging data over time will reduce the impact to the health scorecaused by fluctuations in data. Periods in which the data wasuncharacteristically high (e.g., the person was engaging large amount offitness activity over a short period of time) or uncharacteristicallylow (e.g., person engaged in no fitness activity for a week due to anillness) does not dramatically affect the health score with averagingover time. The health related information can be stored in the memory orin a database accessible by the processor.

The stored data can also be used to predict future health scores for auser. A prediction module can analyze past data (e.g., fitness habits,eating habits, etc.) to extrapolate a predicted health score based on anassumption that the user will continue to act in a predicable manner.For example, if the data shows that a user has exercised one hour everyday for the past thirty days, the prediction module can predict, inaccordance with a prediction algorithm, that the user will continue toexercise one hour for each of the next three days. Accordingly, thescoring module can calculate a predicted health score at the end of thenext three days based on the information from the prediction module. Itcan also factor the prediction into other actions. For example, thesystem can suggest a more exerting physical activity level or challengeto someone who has a high health score but is predicted based on pastexperience to then take a number of days off for recuperation.Furthermore, the system can provide encouragement to the user tomaintain a course of activity or modify behavior. For example, thesystem can send a message to the user indicating that if the userincreased fitness activity by a certain amount of time, the health scorewould go up by a certain amount. This would allow the user set goals toimprove health.

The use of the health score allows for a relative comparison of a user'shealth with that of another person's even though each person can havevery different characteristics, which would make a direct comparisondifficult. For example, a first user (User 1) can have a very differentbody composition or engage in very different fitness activities ascompared to a second user (User 2), which makes direct comparison of therelative health of each user difficult. The use of the health scoremakes comparison of the two users possible with relative ease. In oneexample, User 1 is slightly overweight, which would tend to lower User1's health score. However, User 1 also engages is large amounts offitness activities, thereby raising the user's overall health score. Incontrast, User 2 has an ideal body weight, which would contribute to ahigh health score, but engages in very little fitness activity, therebylowering the health score. User 1 and User 2 are very different in termsof their health related parameters. Accordingly, it would be verydifficult to assess and compare the relative health of User 1 and User2. In accordance with the invention, information related to certainhealth parameters is collected from User 1 and User 2, which is used tocalculate an overall health score. A comparison of User and User 2'shealth score allows for an easy assessment and comparison of the healthof these two users even though they are very different and have verydifferent habits. Therefore, the health score has significant value sothat members of a group can compare their relative health and so thatother entities (e.g., employers, health care insurers) can assess thehealth of an individual. Examples are shown in FIGS. 3 d and 3 e inwhich tabular (current) and graphical (historic, current and predicted)scores of different users are shown. As can be seen in FIG. 3 e, Katrinis expected to surpass the user (Andre) shortly unless he improves hislifestyle and performance. In FIG. 3 d, the impact of the decayalgorithm is illustrated to show the effect on the health score of agiven user (Andre) and the people he has identified as friends. Asnoted, user Andre has a current health score of 669 which situates thisuser between friends Irene (health score 670) and Helle (health score668). The decay algorithm has acted on all of the health scores shown inthe screen shot of FIG. 3 d, as indicated in the “Δ1 Day” column. Moreparticularly, most of the friends of Andre have had their health scorereduced by 1 point due to the reason of “no activity.” A lack of datainput to the system is a basis for the processor executing the decayalgorithm to determine a “no activity” status for a given user. The oneday effect of this status according to the illustrated decay algorithmfor most of the users is a reduction of 1 point in one day, and areduction of 5 points over the course of a week. As such, the decayalgorithm can have a tapering, non-linear impact on an overall healthscore.

As illustrated, user Andre has had moderate activity registered into amemory that is accessible to the system. As a result, the moderateactivity is processed and results in a one day change (delta) that ispositive, and a change that counteracts the influence of the decayalgorithm. Consequently, Andre will be able to observe, as well as thefriends that have access to his published health score, that heincreased his score from 667 to 669 in one day, and from 662 to itspresent value over the past seven days as a result of “moderateactivity.” Moreover, a prediction is computed using the underlyingalgorithm and an extrapolation of data based on most recent reasons(that is, received data) to increase another 5 points. On the otherhand, due to low activity, but a good diet, Helle in the same timeperiod went down 1 point in the last day and a total of 1 point in thelast 7 days and is predicted to lose another point if this ratecontinues. As such, Helle is provided with feedback by execution of thealgorithm and the outputs provided by the system which can encouragemore activity. On the other hand, Irene has no activity and a poor dietwhich results in a more aggressive change to her current health scoreand the longer-view historical and predicted impact on her score. Again,this feedback, which can be provided to users and their friends or tomembers of a group of users that have associated together for achallenge, etc. to provide individual or team motivation to engage infitness activities, eat well, and so on.

Moreover, the health score provides an indication of the relative healthof the individual without revealing the underlying data used tocalculate the health score, which can be sensitive information. Forexample, a user may be uncomfortable revealing his or her weight, age,or amount of time they spend exercising to others persons or entities.Persons can be embarrassed to share his or her weight or the fact thatthey virtually never go to the gym. However, since the health score isderived from several factors, the underlying data used to calculate thescore is kept private. This feature will facilitate the sharing of theuser's overall health because users will not have to disclose privatedata about themselves. For example, a person may be slightly overweight,but she goes to the gym often. Accordingly, that person can receive arelatively good health score. While the person may not want to discloseher weight, she can still disclose her health score which conveysinformation about her relative health without disclosing the underlyingdetails. The intrinsic medical parameters (e.g. weight, height, etc.)and the extrinsic physical activity parameters (e.g. exercise duration,frequency, intensity, etc.) are transformed into a masked compositenumerical value. The masked numerical value is published while thecollected information concerning the intrinsic medical parameters andextrinsic physical activity parameters is maintained private. Theunderlying intrinsic medical parameters and extrinsic physical activityparameters are protected so that a third party is not able to determinethose parameters based on the health score number. This is because theparameters can vary in many different ways and yet the health scorenumber could be the same (e.g., a heavier person that exercisesfrequently can have the same health score as a person that is notoverweight but does not exercise as frequently). Thus, having the healthscore alone does not reveal a person's health related parameters.Accordingly, the underlying health statistics are masked, yet the healthscore can be used as a benchmark to indicate a person's health for avariety of applications.

After the scoring module calculates the health score of the user, atstep 270, a publication module recalls from the memory the designatedgroup of recipients that are authorized to receive the health score. Thegroup of recipients can be friends or family of the user, sportsteammates, employers, insurers, etc. At step 280, the publication modulecauses the health score to be published to the designated group. In thecase that the information is to be published to a group of friends, theinformation can be published to a social networking internet basedportal in which access to the data is limited to those designatedmembers of the group.

The health parameter data and health scores can be stored over time, ina memory or other database, so that a user can track his or herprogress. Charts can be generated in order for a user to track progressand analyze where there can be improvement in behavior. Moreover, trendscan be identified that can lead to the diagnosis of medical problemsand/or eating habits. For example, if a person's weight is continuing toincrease despite the same or increased amount of fitness activity, thesystem can trigger or suggest that they seek certain medical tests (e.g.a thyroid test, pregnancy test) to determine the cause of the weightgain.

In certain implementations, the majority of the system is hostedremotely from the user and the user accesses the system via a local userinterface device. For example the system can be internet based and theuser interacts with a local user interface device (e.g., personalcomputer or mobile electronic device) that is connected to the internet(e.g., via a wire/wireless communication network) in order tocommunicate data with the internet based system. The user uses the localinterface device to access the internet based system in which the memoryand software modules are operating remotely and communicating over theinternet with the local device. The local device is used to communicatedata to the remote processor and memory, in which the data is remotelystored, processed, transformed into a health score, and then provided tothe designated groups via a restricted access internet portal.Alternatively, the system can be primarily implemented via a localdevice in which the data is locally stored, processed, and transformedinto a health score, which is then communicated to a data sharing portalfor remote publication to the designated groups.

The system can be implemented in the form of a social networkingframework that is executed by software modules stored in memory andoperating on processors. The system can be implemented as a separate,stand alone “health themed” social networking system or as anapplication that is integrated with an already existing socialnetworking system (e.g., Facebook, MySpace, etc.). The user is providedwith a homepage in which the user can enter information, manage whichinformation is published to designated groups, and manage the membershipof the designated groups. The homepage includes prompts to the user toenter the health related information for the each of the variousparameters. The user can enter his or her weight, date of birth, height,fitness activity, and other health related information. The user'shealth score is then calculated. The health score is shared with otherusers that are designated as part of a group permitted to have access tothat information. Moreover, the user can view the health scoreinformation of others in the group. Accordingly, the user is able tocompare his or her overall health with the health of others in thegroup. Comparison of health scores with others in the group can providemotivation to the individuals in the group to compete to improve theirhealth scores. Other information, such as health tips, medical news,drug information, local fitness events, health services, advertising anddiscounts for medical and/or fitness related supplies and service,issuance of fitness challenges or health related goals, for example, canbe provided via the homepage.

In further implementations, the health score can be a composite of aMetric Health Model score and a Quality of Life Model score. Combiningscores from multiple models provides a more holistic assessment of auser's health. The Metric Health Model score assesses a user's healthbased on relatively easily quantifiable parameters (e.g., age, sex,weight, etc.) and compares those numbers to acceptable populations studymodels. The Quality of Life Model score focus on a user's self-assessedquality of life measure based on responses to a questionnaire (i.e., thesystem takes into account the user's own assessment of their health andlife quality) because there are correlations between how an individual“feels” about his or her life and a realistic measure of health. Acombination of the scores from these two models, which will be discussedin more detail below, provides a more inclusive and holistic assessmentof health.

The Metric Health Model score is based on medical parameter informationof a user, such as their medical history information, attributes,physiological metrics, and lifestyle information to the system. Forexample, the system can provide the user a questionnaire to promptresponses (yes/no, multiple choice, numerical input, etc.) or providethe user with form fields to complete. Medical history information caninclude the user's history of medical conditions and/or the prevalenceof medical conditions in the user's family. Examples of medical historyinformation can include information such as whether the user hasdiabetes, has direct family members with diabetes, whether the user orfamily members have a history of heart attack, angina, stroke, orTransient Ischemic Attack, a history of atrial fibrillation or irregularheartbeat, whether the user or family members have high blood pressurerequiring treatment, whether the user or family members havehypothyroidism, rheumatoid arthritis, chronic kidney disease, liverfailure, left ventricular hypertrophy, congestive heart failure, regularuse of steroid tablets, etc.

The Metric Health Model score can also be based on user attributes. Theattributes can include age, sex, ethnicity, height, weight, waist size,etc. In addition, Metric Health Model score can be based onphysiological metrics of the user. Examples of physiological metrics caninclude systolic blood pressure, total serum cholesterol, high-densitylipoprotein (HDL), low-density lipoprotein (LDL), triglycerides,high-sensitivity C-reactive protein, fasting blood glucose, etc. Theinputs can also include parameters of a user's lifestyle. For example,lifestyle parameters can include inputs about whether the user is asmoker (ever smoked, currently smokes, level of smoking, etc.), how muchexercise the user performs (frequency, intensity, type, etc.), type ofdiet (vegetarian, high-protein diet, low-fat diet, high-fiber diet,fast-food, restaurant, home cooking, processed and pre-packaged foods,size of meals, frequency of meals, etc.). These are some of the examplesof parameters that can be used to compare the user's health indicatorsto survival probability models in order to calculate the user's MetricHealth Model score.

Survival probability prediction models can be used to predict theprobability that an individual will suffer one or more serious healthevents over a given time period. Mathematical models can estimate thisprobability from observed population characteristics. Usingobservational data on a set of unambiguous severe health events, such asstroke or cardiac infarction, models can generate the probability thatan individual will suffer one such event over a given time horizon froma set of measurements of markers, or predictors, for the event (e.g.,information about a user's medical history, attributes, physiologicalmetrics, lifestyle, etc. as described above). The time distance betweenthe moment the predictors are measured, and the target event that isgenerated by such models, is referred to as a survival probability,although it must be understood that not all target events considered arenecessarily fatal.

These survival probability models are typically derived from the studyof generally large populations that are followed for a considerablelength of time, usually more than ten years, and the statisticscollected on the observation of the target event(s) are summarized andgeneralized using mathematical methods. There are a number of suchmodels that exist that have been extensively validated and maintainedand improved by periodically updating the model's parameters using newdata. Examples of existing models can include a subset of modelsdeveloped and maintained by the Framingham Heart Study (an extensivebibliography on results obtained from the Framingham Heart study isavailable at www.framinghamheartstudy.org/biblio), a subset of themodels developed and maintained by the University of Nottingham and theQResearch Organization (see, for example, J Hippisley-Cox et al,Predicting cardiovascular risk in England and Wales: prospectivederivation and validation of QRISK2, BMJ 336: 1475 doi:10.1136/bmj.39609.449676.25 (Published 23 Jun. 2008)), the ASSIGN modeldeveloped by the University of Dundee (see, for example, HTunstall-Pedoe et al, Comparison of the prediction by 27 differentfactors of coronary heart disease and death in men and women of theScottish heart health study: cohort study; BMJ 1998; 316:1881), theReynolds model (see, for example, PM Ridker et al, C-Reactive Proteinand Parental History Improve Global Cardiovascular Risk Prediction: TheReynolds Risk Score for Men, Circulation 2008; 118; 2243-2251, andDevelopment and Validation of Improved Algorithms for the Assessment ofGlobal Cardiovascular Risk in Women, JAMA, Feb. 14, 2007—Vol 297, No.6), the PROCAM model from the Munster Heart Study (see, for example,Simple Scoring Scheme for Calculating the Risk of Acute Coronary EventsBased on the 10-Year Follow-Up of the Prospective Cardiovascular Münster(PROCAM) Study, Circulation. 2002; 105:310-315), and the SCORE model(see, for example, R M Conroy et al, Estimation of ten-year risk offatal cardiovascular disease in Europe: the SCORE project, EuropeanHeart Journal (2003) 24, 987-1003). Other constituent risk models canalso be included. In addition, precursor models can also be used.Precursor models predict the development of a first condition (e.g. highblood pressure), wherein the development of the first condition ispredictive of developing a second condition (e.g., heart disease). Thereare models that generate estimates of the probability of developingdiabetes or high blood pressure, for example, which are two importantpredictors of mortality. A high probability of developing diabetes infive years, for instance, will independently increase the probability ofa serious cardiovascular event within the next ten years. Several suchprecursor models can be included and the inclusion of precursor modelsleads to more accurate metric risk models, but more importantly, alsoleads to the possible reduction of the risk of mortality throughwell-defined modifiable aspects of lifestyle.

Traditional survival probability models have certain inherentlimitations that result from the procedures used to build them. Inderiving such models, researchers compromise between accuracy andusability. It is difficult for an inductive model, meaning a modelderived directly from data, to include all possible predictors. This isin part because not all relevant predictors of a particular event areknown, but also in part because some known predictors may be difficultor expensive to measure. In fact, several well-known markers of risk,such as genetic factors, are often not included in such models.Therefore, several potential and known predictive metrics can beexcluded as covariates when deriving a given survival model.

Survival probability models are built using data collected from a givenpopulation, and thus summarize and generalize morbidity and mortalitycharacteristics of the studied population. However, such a model mightbe at variance when compared with risk estimates derived from otherpopulations. When a given model is used in a population that differsfrom the one where the model was built it often underestimates oroverestimates a particular risk because only a few predictors are oftenconsidered, and because other relevant predictors that may not beincluded in the model might very well differ between two populations.

Given the above discussion, together with basic probabilistic logic, ajudicious combination of models derived for several differentpopulations will generate a better view of the risks that an individualpicked at random is exposed to, and will thus be more robust inestimating risks for the population at large. Furthermore, based onmathematical grounds, under very general assumptions, certain modelcombination methods, referred to as predictor boosting, can improve theaccuracy of the constituent models. In fact, boosting a set of models,when done correctly, will produce a model with accuracy that is, atworst, equal to that of the most accurate model in the boosted set.

Accordingly, the Metric Health Model score can be calculated bycomparing the user's medical parameter information to the survivalprobability models. A score, preferably in the range of 0 to 1000, withthe top end signifying perfect health and the low side signifying poorhealth, can be derived following a two-step process. First, an overallsurvival probability is obtained from a combination of the survivalprobabilities generated by individual survival probability models, asdescribed above. Second, the resulting survival probability, which is anumber in the 0 to 1 range, is transformed using a parametric nonlinearmapping function into the 0 to 1000 range. The parametric mappingfunction is tuned so that it is linear, with a high slope, in the regionof typical survival probabilities, and asymptotically slopes off in thelow and high ends of the survival probability distribution. The mappingfunction is designed to be strongly reactive to changes in the typicalsurvival probability region.

As discussed above, the health score can be composed of the MetricHealth Model score, and also the Quality of Life Model score. TheQuality of Life Model score is based on a user's answers to a set ofquestionnaires. The system can include several different questionnaireswith some questions in common. The type of questionnaires and the typeof questions therein presented to the user can be tailored based on auser's health parameters (i.e., user age, other data in the user'smedical history, etc.). A specific questionnaire can be generated andpresented to the user on the basis of information on the user that isknown to the system. The questions can be presented with an appropriatemultiple choice response that the user can check/tick on a form, with nofree-form text is entered by the user to permit easier assessment of theresponses. Other types of responses are possible (e.g., rating how truea statement is to the user 1-10). The following list provides severalsample questions (in no particular order) on a number of health-relatedquality of life topics that can be used in a system questionnaire.

Sample Questions:

-   -   How do you rate your quality of life?    -   How do you rate your overall health?    -   How much do you enjoy life?    -   To what extent do you feel your life to be meaningful?    -   How well are you able to concentrate?    -   How safe do you feel in your daily life?    -   How healthy is your physical environment?    -   Are you satisfied with your appearance?    -   To what extent do you have the opportunity for leisure        activities?    -   How much do you need any medical treatment to function in your        daily life?    -   For how long have your activities been limited because of your        major impairment or health problem?    -   Do you need help in handling your personal care needs because of        health problems?    -   Do you need help in handling your routine needs because of        health problems?    -   Are you limited in any way in any activities because of any        major impairment or health problem?    -   How true or false is each of the following statements for you?:        -   I seem to get sick a little easier than other people        -   I am as healthy as anybody I know        -   I expect my health to get worse        -   My health is excellent —Do you suffer from any of the            following major impairment or health problem that limits            your activities?:        -   Arthritis or rheumatism        -   Back or neck problem        -   Cancer        -   Depression, anxiety or any emotional problem        -   Vision problem        -   Fractures, bone/joint injury        -   Hearing problem        -   Breathing problem        -   Walking problem        -   Other impairment or problem    -   During the past 30 days, for about how many days:        -   was your physical health not good?        -   did pain make it hard for you to do your usual activities,            such as self-care, work, or recreation?        -   have you felt sad, blue or depressed?        -   have you felt worried, tense or anxious?        -   have you felt you did not get enough rest or sleep?        -   have you felt very healthy and full of energy?        -   have you been a very nervous person?        -   have you felt so down in the dumps that nothing could cheer            you up?        -   have you felt calm and peaceful?        -   did you have a lot of energy?        -   have you felt downhearted and blue?        -   did you feel worn out?        -   have you been a happy person?        -   did you feel tired?        -   How satisfied are you with:        -   your sleep?        -   your ability to perform your daily living activities?        -   your capacity for work?        -   yourself?        -   your personal relationships?        -   your sex life?        -   the support you get from your friends?        -   the conditions of your living place?        -   your access to health services?        -   your transport?    -   Are you limited in any of the following activities because of        your health?:        -   Vigorous activities, such as running, lifting heavy objects,            participating in strenuous sports        -   Moderate activities, such as moving a table, pushing a            vacuum cleaner, bowling, or playing golf        -   Lifting or carrying groceries        -   Climbing several flights of stairs        -   Climbing one flight of stairs        -   Bending, kneeling or stooping        -   Walking more than a mile        -   Walking several blocks        -   Walking one block        -   Bathing or dressing yourself

This list above is just a sample of questions that can be presented to auser. The user's responses to the questions are assigned a value. Forexample, each of the multiple choice responses can be assigned aparticular value, and all of the user's response can be tallied togenerate a score. In addition, different questions and differentresponses can be weighted differently since some questions, or theseverity of the response, can have a greater predictor of the user'shealth. The system can also assign a value based on the user's responseto a combination of questions, because certain combinations can be morepredictive of health. Accordingly, by assessing the user's responses tothe questionnaire a Quality of Life Model score can be derived.Preferably, the Quality of Life Model score is a numerical value in therange of 0 to 1000.

The health score is computed as a weighted average of the Metric HeathModel score and the Quality of Life Model score. The health score can bepresented to the user. The health score can be presented as a numericalvalue, as a graphic value (i.e. as a meter, bar, or slider), or acombination of the both, for example. Referring to FIG. 3A, the healthscore is presented by a combination of a numerical score 302 and aslider 304. The slider can also be color-coded to indicate the score.The position of the slider bar 306 indicates the user's score.

One advantage of the presentation of the health score is that it is notnecessary to present the survival probabilities and raw metrics to theuser. Instead, users are presented with a standardized score.Preferably, this is true of the overall Metric Heath Model and Qualityof Life scores, but it is also true of the relevant model inputs. Thisis done mainly to standardize all output, in the sense that users do notneed to know whether high values of a particular input variable are goodor bad; in all cases, high scores of any input value lead to higheroverall health score values, and low input variable scores lead to loweroverall values of the health score.

Furthermore, another advantage of the standardized health scores is thatusers can compare health scores against other users. This allows forcomparative bench marking (against friends, co-workers, etc.) with otherusers. Such score comparisons can be a part of a game component of thesystem in which the user competes against other users, as will bedescribed in more detail below. Gaming aspects of the system can be usedmotivate the user of the health score system, such as a comparison ofscores among user-selected groups, comparison of individual scoreswithin configurable subpopulation distributions, time-tracking ofscores, and setting of goals, among others. Referring to FIG. 3B, theusers numerical score 302 and graphical score 306 are presented incombination with a range of scores 308 from a group (e.g. the world) sothat the user can see how his/her score compares to others in the group.The gaming incentives can be extended by users to allow the comparisonof health scores between users that can differ substantially in one ormore of several specific input parameters, such as age, weight, andprior risk conditions. The system highlights improvements in modifiableuser metrics, particularly in lifestyle components, and theseimprovements in score provide user incentives. This allows faircompetition between users of a father and his children, for example, viathe health score. In one aspect, the health score provides equalizationbetween users of different characteristics and is thus similar to thatof a handicap in some sports. Referring to FIG. 3C, the user's score 306is compared to the scores 310 a-e of a user selected group of friends.Referring to FIG. 3D, the user's individual medical parameters (e.g.,the medical data provided as a part the Metric Health Model) can becompared against other users graphically without revealing theunderlying actual values. The high-density lipoprotein (HDL) level,low-density lipoprotein (LDL) level, systolic blood pressure (sBP),diastolic blood pressure (dBP), body mass index (BMI), and fasting bloodglucose (fBG) level are shown on a graph 312. The user's scores arerepresented by a line 314, the user's friends scores are eachrepresented by a different dot 316, and a distribution block 318 for alarger population group (e.g., Switzerland) is also shown. Thus, theuser can compare their individual parameters to a group of friends andthe average for a large population group.

Users can input data into the system at the time of an event (i.e.,exercise event, food consumption, blood pressure measurement, etc.), andsee the resulting update of their health score in real-time. The systemcan include drill-down capabilities, allowing users to see the varioushealth score component scores, including tracking over time and thecorresponding trends in all scores; it also includes the setting ofgoals on the various scores.

As an example of use of the system, upon registration with the system(e.g., the initial use of the system), a user is prompted to providemedical history data. The user is also prompted to respond to a completeQuality of Life questionnaire selected by the system for the given userbased on the medical history and user parameters supplied by the user.After the registration, at periodic intervals, users are presented withshort subsets (3 to 5 questions) of their custom Quality of Lifequestionnaire to keep their responses up to date and track changes.Users can enter inputs for Metric Health Model at any time, and thesystem prompts the user for values that have not been updated for sometime. Inputs to the Metric Health Model can be acquired automatically bythe system by accessing a series of digital measuring devices that havebeen integrated into the system (e.g., the system can comprise a mobileelectronic communication device, for example, a smart phone, that is inwireless communication with a measurement device, such as a bloodglucose monitor, so that parameters can be measured, transmitted, andstored by the system). These can include weight, blood glucose, physicalactivity, and other parameters. Several or multifunction digitalmeasurement devices can be included in the system. In the case ofmedical parameters that are more difficult to obtain with a homemeasuring device, such as serum lipid concentration levels, users areonly prompted to provide the relevant data once per (system) configuredtime period (e.g., annually and coinciding with a user's routinephysical medical examination).

To avoid false scores, the system can include several algorithms toassess the validity of user inputs. The validation methods can rangefrom ones based on outlier detection to ones based on multidimensionallikelihood estimators. When the system detects a possible bad inputvalue it flags it and prompts the user to either confirm the value or toenter a new one.

The system can generate all its scores, even when missing one or moreinputs. It does this by imputing the missing value or values using avariety of statistical methods that range from ones based on globalpopulation statistics, to methods based on the use of more complicatedstatistical models that are built into the platform. However, wheneverinputs include imputed values, the system clearly flags all affectedscores, and periodically alerts the user to provide the missing data.The system can also allow for score simulation, in which the user cantemporarily adjust his or her parameters so that a user can see howchanging certain parameters (e.g., losing weight) affects the user'sscore.

The system can also provide recommendations to the users to take certainactions that can improve the user's health score. These recommendationscan be very specific when any input variable is in its danger zone, andmore generic when any input variable is outside its optimal range.

As discussed above, the health score can be used as a part of a game orcompetition aspect of the system. The game aspect increases the funelement of the system for the user and increases the user's affinity tocontinue to use the system. The game aspect can come in the form ofobtaining higher levels based on achievements, competing against others(e.g., in a league), and/or completing challenges. The “level” is anoverall indication of progress. The level can be monotonicallyincreasing and will increase by gaining activity points. Activity pointscan be gained from performing numerous activities, such as time spentperforming fitness activities (e.g., exercising), improving one's healthscore, improving one's BMI, taking part in discussions on the system(e.g., the system can be a web-based social networking platform anddiscussions or “classes” can be offered to teach fitness skills). Auser's level can be displayed on a user's profile and in discussionposts so that other users can see each other's level. A user's levelstatus can also provide access to specific items, system features andfunctionality, or rewards (e.g., branded apparel).

Users can also compete within leagues in the system. The leagues arecomposed of groups of users and the users within the league can competeagainst each other (as part of a team or individually). The leagues cancompete for a limited time (e.g., monthly) and the leagues can bedesignated based on the level of the users (using the level of the useras discussed above), the type of activity being performed in the league,and the geographic region of the users. For example, one particularleague can be the “bronze” (level) “mountain biking” (sport) “GreaterZurich Area” (region) league and a user's success in this league ismeasured by the distance traveled and elevation climbed (measuredquantity). Thus, bronze level users living in the Greater Zurich Areathat are interested in mountain biking can compete in this league.Limiting the leagues to a particular region gives the users something torelate with and all the users can share in common, and further allowsusers to meet face to face (e.g., for group exercise events). One issuewith one big international league is that such a league may seemanonymous, crowded and meaningless to some users (members competingagainst members residing on completely different continents withlanguage barriers can inhibit group or team mentalities). Limitingleagues to particular level brackets equalizes the playing field forusers of particular skill levels. Quantities to be measured to determineperformance in the league can include distance (horizontal, vertical)and duration of fitness activity performed, for example. Users can alsoform teams within the leagues. Team leagues work in the same way as theleagues outlined above, however the ranking is based on the team'soverall performance. Teams increase the communal aspect of participationin the activity. Teams can be fixed in size (e.g., 2, 3, 5, 10, etc.users).

Users can also be presented by the system with challenges to complete.The challenges can set forth a time period for completion of a goal. Thegoals of the challenge can be, for example, healthscore improvement(normalized), completion of sport-related activity parameters (e.g.,total distance, total climbing, etc.), or completion of a sport-relatedactivity within a specific period of time (e.g., complete six minutemile on a specific route). The challenge can be public and any user canparticipate, or limited to a group (e.g. friends, co-workers, socialgroup, etc.) As an example, a particular public challenge can be aninline skating challenge in New York City for the route around theCentral Park Loop measuring the time taken for completion. Publicchallenges can be generated automatically by the system or by systemadministrators. Group challenges can be issued by group members.Challenges provide strong appointment dynamics, encouraging users tocommit to exercise. Challenges (typically) have a lower time commitmentthan leagues. Route selection can be automated with the community. In afirst step, the community can publish routes on the system platform(e.g., a social networking type website); in a second step, the systemselects popular routes (i.e. routes with high user activity) as weeklychallenges. Route validation is done by GPS tracking. Challenges can besafety screened to prevent the promotion of unduly risky challengeactivities, such mountain biking dangerous downhill routes.

The league and challenge systems provide opportunities to grantachievements. Achievement status indications can be collected anddisplayed on a user's profile. Achievements are much like a trophy,medal, or award provided to the user for completing challenges and/orsucceeding in a league activity. Many different achievements arepossible, such as related to the number of friends the user has on thesystem (community participation), achievements related to the time,intensity, and number of fitness activities engaged in (level of fitnessparticipation), achievements related to specific sport activities (e.g.,distance run), the frequency a user measures their parameters (e.g.,weight) in order to keep the system up to date, the amount of weightlost, or the ability to maintain ones BMI, for example. The followinglist is an exemplary set of achievements and the activities required toearn the achievements:

Exemplary Achievement List:

-   -   Challenger: Take part in a public challenge.    -   Accomplished Challenger: Take part in 10 public challenges.    -   Champion: Win a challenge.    -   Multi-sport Champion: Win a public challenge in two different        sports.    -   International Challenger: Take part in a public challenge in two        different countries.    -   International Champion: Win a public challenge in two different        countries.    -   World Wide Challenger: Take part in a public challenge on each        continent.    -   World Wide Champion: Win a public challenge on each continent.

Other aspects of the challenge and league systems are that the systemscan be tied to marketing opportunities. For example, marketers cansponsor prizes for the winners of a challenge. The prize can be relatedto the challenge (e.g., gift certificate to health food score for winnerof weight loss challenge). In addition, challenge routes can be selectedto direct users to certain areas to increase tourism or to begin/end atselected destinations (e.g., bike challenge begins in front of sportsequipment store).

One advantage of the system is that it provides users and groups ofusers with benchmarking capabilities. It allows other groups, such asinsurance carriers or employers, to assess the relative health ofindividuals in order to determine the health related risks of eachindividual. Accordingly, users can compare themselves against others inorder to assess their comparative health level amongst a group offriends. Insurance carriers can use the health score information to setpremiums for an individual or a group of individuals (e.g. employees ofa company). In other implementations, health scores can be provided fora group based on the health scores of the individuals in the group. Forexample, a health score can be calculated for a company based on itsemployees so that an insurance carrier can set premiums based on thehealth score of the company compared to other companies. In furtherapplications, the health score can be used for assessing the health ofprofessional athletes to determine the athlete's real market value. Vastamounts of money and resources are invested in athletes at all levels inprofessional sports. A large component of the decision about investingin an athlete is based on the past performance of the athlete. Otherfactors can include past physical injury history and the athletesubmitting to a physical exam before the deal is complete. The healthscore can be used as an indicator of the athlete's current health andused as a predictor of the athletes future performance. If the athlete'shealth score were low, this can indicate that the athlete is more proneto suffering an injury or that physical performance will diminish.Accordingly, the health score can form a basis for a decision on whetherto invest in an athlete. The health scores could also be used as apredictor of the outcome of a particular game played between two teams.For example, the health scores of the individual team members can beaggregated in order to provide a team health score. A comparison of theteam health scores can be indicative of the likely outcome of the gamebetween the two teams (e.g., the team with highest health score may bemore likely to win). Such information can be used in gaming contextssuch as fantasy sports teams, or in order to set odds for sportsbetting. The health score could be used for club competitions (e.g.,group health improvement competitions, advertising based on a person'shealth score, gaming, tv/internet, etc.

Thus, in a broad aspect, a method according to the invention can beunderstood as collecting health related information, processing theinformation into a health score, and publishing the health score isprovided. A system for implementing the method can include a computerhaving a processor, memory, and code modules executing in the processorfor the collection, processing, and publishing of the information.Information concerning a plurality of health related parameters of auser is collected, particularly, both intrinsic values concerning themeasurable, medical parameters of at least one natural person, and theextrinsic values concerning the activities of each such person(s) suchas the exercise performed, the type of job the person has and the amountof physical work associated with the job (e.g. sedentary, desk jobversus active, manual labor intensive job) and/or the calories/foodconsumed. Weighting factors are applied to the health related parameterin order control the relative affect each parameter has on the user'scalculated health score. The health score is computed using theprocessor by combining the weighted parameters in accordance with analgorithm. The health score is published to a designated group via aportal. In one implementation, the portal is an internet basedinformation sharing forum.

As such, the invention can be characterized by the following points in amethod for collecting and presenting health related data:

-   -   collecting information concerning a plurality of health related        parameters of a user;    -   storing the collected information in a memory;    -   storing weighting factors in the memory;    -   processing the collected information by executing code in a        processor that configures the processor to apply the weighting        factors to the health related parameters;    -   computing a health score using the processor by combining the        weighted parameters in accordance with an algorithm; and    -   providing the health score to a designated group via a portal.

The methods described herein have been described in connection with flowdiagrams that facilitate a description of the principal processes;however, certain blocks can be invoked in an arbitrary order, such aswhen the events drive the program flow such as in an object-orientedprogram implementation. Accordingly, the flow diagrams are to beunderstood as example flows such that the blocks can be invoked in adifferent order than as illustrated.

While the invention has been described in connection with certainembodiments thereof, the invention is not limited to the describedembodiments but rather is more broadly defined by the recitations in anyclaims that follow and equivalents thereof.

1. A computer implemented method for processing private health relateddata into a masked numerical score suitable for publishing, comprisingthe steps of: receiving data into a memory, wherein the received datarepresents at least one intrinsic medical parameter and at least oneextrinsic physical activity parameter of a user; storing the receiveddata in the memory; storing weighting factors in the memory; processingthe received data by executing code in a processor that configures theprocessor to: apply respective ones of the weighting factors to the atleast one intrinsic medical parameter and the at least one extrinsicphysical activity parameter, and apply a decay component to theprocessed at least one extrinsic physical activity parameter to reducethe relative weight of the processed at least one extrinsic physicalactivity parameter for a physical activity in dependence on at least onefactor associated with the user; transform the processed received databy executing additional code in the processor, wherein the processedreceived data are transformed into a masked composite numerical value bycombining the weighted parameters in accordance with an algorithm; andautomatically publish the masked composite numerical value to adesignated group via a portal, using code executing in the processor andfree of human intervention, while maintaining the received datarepresenting the at least one intrinsic medical parameter and the atleast one extrinsic physical activity parameter private.
 2. The methodof claim 1, wherein the at least one factor associated with the user isan age or an age range of the user such that the decay component reducesthe relative weight of the processed at least one extrinsic physicalactivity parameter for a first user of a first age or age rangedifferently than for a second user of a second age or age range.
 3. Themethod of claim 1, further comprising the step of averaging thepublished masked composite numerical value of a group of users todetermine a group composite numerical value using further code executingin the processor.
 4. The method of claim 1, further comprising the stepsof: receiving data into the memory representing at least one extrinsiclifestyle parameter of the user, wherein the step of processing thereceived data further includes executing additional code in theprocessor that configures the processor to: apply respective ones of theweighting factors to the at least one extrinsic lifestyle parameter, andapply a decay component to reduce the relative weight of the at leastone extrinsic lifestyle parameter in dependence on the at least onefactor or at least one other factor associated with the user, whereinthe step of transforming the processed received data further includesexecuting code in the processor that configures the processor to combinethe at least one processed intrinsic medical parameter, the at least oneprocessed extrinsic physical activity parameter and the at least oneprocessed extrinsic lifestyle parameter in accordance with thealgorithm.
 5. The method of claim 1, wherein the steps of processing,transforming and publishing are performed substantially automaticallyupon receipt of any of the received data, and further comprising:communicating either the processed received data or the masked compositenumerical value to an exercise machine and automatically establishing anexercise program on that basis, and communicating activity informationfrom the exercise machine to the memory for inclusion among the at leastone extrinsic physical activity parameter.
 6. (canceled)
 7. The methodof claim 1, further comprising monitoring the composite numerical valueand causing triggering of a feedback communication by executing code inthe processor and without human intervention, wherein the feedbackcommunication is operative to provide an alert to the user to initiate aphysical activity or change a scheduled physical activity, or thefeedback communication comprises an alert sent to a predeterminedperson.
 8. (canceled)
 9. (canceled)
 10. The method of claim 7, whereinthe step of monitoring comprises monitoring value over time andtriggering alert in dependence on change over time.
 11. The method ofclaim 7, wherein the step of triggering a feedback communicationcomprises sending an electronic communication directed to the userincluding directions on changes to the user's physical activity and/orlifestyle for improving the masked composite numerical value.
 12. Themethod of claim 7, further comprising calculating, by executingadditional code in the processor, a predicative masked compositenumerical value, which is indicative of a predicted future state basedon past data, using the received data of the user in accordance with apredicative algorithm and causing triggering of a predictive feedbackcommunication.
 13. The method of claim 1, wherein the step of processingthe received at least one extrinsic physical activity parameterincludes: obtaining a measure of calories expended in the physicalactivity into the memory; and executing further code in the processorthat configures the processor to: transform the measured calories into ametabolic equivalent, MET, value by dividing by the user's body weight;divide the MET value between a health pool and a bonus pool, wherein thebonus pool has a predetermined size and any divided MET value exceedingthe bonus pool size is allocated to the health pool; and apply a dailydecay component to the bonus pool; wherein the step of transforming theprocessed data comprises combining the processed at least one intrinsicmedical parameter and a weighted health pool value in accordance withthe algorithm.
 14. A health monitoring system comprising: acommunication unit operable to receive data on at least one intrinsicmedical parameter and at least one extrinsic physical activity parameterof a user; a memory arranged to store the received data and to storeweighting factors; a processor arranged to process the received data byexecuting code that configures the processor to: apply respective onesof the weighting factors to the at least one intrinsic medical parameterand the at least one extrinsic physical activity parameter, apply adecay component arranged to reduce the relative weight of the processedat least one physical activity parameter for a physical activity independence on at least one factor associated with the user; theprocessor being further arranged to execute code to transform theprocessed received data into a masked composite numerical value usingthe processor by combining the weighted parameters in accordance with analgorithm; and a portal arranged to publish the masked compositenumerical value to a designated group while maintaining the receiveddata representing the at least one intrinsic medical parameter and theat least one extrinsic physical activity parameter private.
 15. Thesystem of claim 14, wherein the at least one factor associated with theuser is an age or an age range of the user such that the decay componentreduces the relative weight of the processed at least one extrinsicphysical activity parameter for a first user of a first age or age rangedifferently than for a second user of a second age or age range.
 16. Thesystem of claim 14, wherein the communication unit is further arrangedto receive data on at least one extrinsic lifestyle parameter of a user,wherein the processor is further arranged to execute code to: applyrespective ones of the weighting factors to the at least one extrinsiclifestyle parameter, apply a decay component to reduce the relativeweight of the at least one extrinsic lifestyle parameter in dependenceon the at least one factor or at least one other factor associated withthe user, and transform the processed data by combining the at least oneprocessed intrinsic medical parameter, the at least one extrinsicphysical activity parameter and the at least one extrinsic lifestyleparameter in accordance with an algorithm.
 17. (canceled)
 18. The systemof claim 14, further comprising a remote user device, the system beingarranged to communicate with the remote user device during physicalactivity to receive at least selected ones of the at least one extrinsicphysical activity parameter.
 19. The system of claim 14, furthercomprising a monitoring unit arranged to monitor the composite numericalvalues and being arranged to cause triggering of a feedbackcommunication upon detecting a predetermined event associated with themonitored composite numerical values.
 20. The system of claim 19,wherein the feedback communication is operative to re-configure aprogram to define or that is defining a scheduled physical activity forthe user.
 21. The system of claim 19, wherein the monitoring unit isarranged to cause transmission of an electronic communication directedto the user including directions on changes to the user's physicalactivity and/or lifestyle for improving the masked composite numericalvalue.
 22. The system of claim 19, wherein the processor is furtherarranged to execute code that configures the processor to calculate apredicative masked composite numerical value that is indicative of apredicted future state based on past data, using the received data ofthe user in accordance with a predicative algorithm and wherein themonitor is arranged to cause triggering of a predictive feedbackcommunication.
 23. The system of claim 14, wherein the processor isarranged to process the received at least one extrinsic physicalactivity parameter by executing code that configures the processor toperform steps including: obtaining a measure of calories expended inphysical activity; transforming the measured calories into a metabolicequivalent, MET, value by dividing by the user's body weight; dividingthe MET value between a health pool and a bonus pool, wherein the bonuspool has a predetermined size and any divided MET value exceeding thebonus pool size is allocated to the health pool; applying a daily decaycomponent to the bonus pool; and transforming the processed data bycombining the processed at least one intrinsic medical parameter and aweighted health pool value in accordance with the algorithm.
 24. Thesystem of claim 14, further comprising a bi-directional communicationlink to an exercise machine that is configured to: communicate eitherthe processed data or the masked composite numerical value to theexercise machine; automatically establishing the exercise program on thebasis of the communicated data or the masked composite numerical value,and receive from the exercise machine into the memory activityinformation for inclusion among the at least one extrinsic physicalactivity parameter.